Department of Computing, Imperial College London, London, SW7 2RH, UK.
Sci Data. 2015 Mar 31;2:150007. doi: 10.1038/sdata.2015.7. eCollection 2015.
Many countries are rolling out smart electricity meters. These measure a home's total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their energy efficiency when provided with itemised, appliance-by-appliance consumption information. Energy disaggregation is a computational technique for estimating appliance-by-appliance energy consumption from a whole-house meter signal. To conduct research on disaggregation algorithms, researchers require data describing not just the aggregate demand per building but also the 'ground truth' demand of individual appliances. In this context, we present UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances. This is the first open access UK dataset at this temporal resolution. We recorded from five houses, one of which was recorded for 655 days, the longest duration we are aware of for any energy dataset at this sample rate. We also describe the low-cost, open-source, wireless system we built for collecting our dataset.
许多国家正在推出智能电表。这些电表可以测量家庭的总电力需求。然而，对消费者行为的研究表明，当提供分项的、按设备划分的能耗信息时，消费者能够最大程度地提高能源效率。能源分解是一种从整个房屋仪表信号估算设备级能耗的计算技术。为了对分解算法进行研究，研究人员需要的数据不仅要描述每栋建筑的总需求，还要描述单个设备的“实际”需求。在这种情况下，我们提出了 UK-DALE：这是一个来自英国的开放数据集，以 16 kHz 的采样率记录整个房屋的电量，并以 1/6 Hz 的采样率记录各个设备的电量。这是第一个在这个时间分辨率上开放获取的英国数据集。我们从五所房屋中进行了记录，其中一所记录了 655 天，这是我们所知的在这个采样率下任何能源数据集的最长记录时间。我们还描述了我们为收集数据集而构建的低成本、开源、无线系统。
